# Classic Fransis Galton heights of fathers and sons data set and the simple regression

R codes to get the data and run the regression

names(father.son)[2]<-“hson”
names(father.son)[1]<-“hfather”
plot(hson ~ hfather, data=father.son, bty=”l”, pch=20)
abline(a=0,b=1,lty=2,lwd=2) # what is the hypothesis here? Why we are looking at this line
abline(lm(hson ~ hfather, data=father.son),lty=1,lwd=2) # what is the hypotheses here? which one to use – this or the previous one?

The outputs are:

Call:
lm(formula = hson ~ hfather, data = father.son)

Coefficients:
(Intercept)      hfather
33.8866       0.5141

The graphical output is

# AI – Will it get wild? Why Will it Not…?

Will it get wild. Why will it not…?

(A small sample of Google images on “AI”)

First premise for deciphering human interpreted conscious machine: Consciousness(hierarchical levels of evolution) === hierarchical layers of latent dimensions (hierarchical average statistical dimensions)

Second premise for deciphering human interpreted conscious machine: Greed(fear of the future traps), fear(inability traps), gluttonous desire(existence traps), love(network traps), procreation(perpetuity traps) brings errors in judgment creating variations and trap holes keeping lower levels of outcome

Third premise for deciphering human interpreted conscious machine: The environment. The levels of certainty will influence in the variations of manifested sequence of events connected with mechanical consciousness.

Our children are our creations so are our children’s children.  Mechanical consciousness is our thought children. If it does not carry our thought patterns constrained by the above premises, then it does not exist.

The interpretation of existence, and hence the implementation of the statistical mechanics of AI, is very much our perception and we exist because we interpret.

Will it get wild. Why will it not…?   It is in our genes, and we consider that as the opportunity of living the time.